UCL logo
skip to navigation. skip to content.

Gatsby Computational Neuroscience Unit




UCL Home
  • UCL Home
  • UCL Gatsby Computational Neuroscience Unit
UCL Gatsby Unit
  • introduction
  • people
  • research
  • publications
  • courses
  • phd programme
  • events
  • directions
  • greater gatsby
  • vacancies
  • Internal
  • ucl

 

 

  • Home
  • Staff & Students
  • Vacancies

 

Franz Király

 

 

Wednesday 30th July 2014

Time: 4pm

 

Basement Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

Learning with Cross-Kernels and Ideal PCA


We describe how cross-kernel matrices, that is, kernel matrices between the data and a custom chosen set of `feature spanning points' can be used for learning. The main potential of cross-kernels lies in the fact that (a) only one side of the matrix scales with the number of data points, and (b) cross-kernels, as opposed to the usual kernel matrices, can be used to certify for the data manifold. Our theoretical framework, which is based on a duality involving the feature space and vanishing ideals, indicates that cross-kernels have the potential to be used for any kind of kernel learning. We present a novel algorithm, Ideal PCA (IPCA), which cross-kernelizes PCA. We demonstrate on real and synthetic data that IPCA allows to (a) obtain PCA-like features faster and (b) to extract novel and empirically validated features certifying for the data manifold.

[Joint work with Louis Theran, Martin Kreuzer.]

 

 

 

  • Disclaimer
  • Freedom of Information
  • Accessibility
  • Privacy
  • Advanced Search
  • Contact Us
Gatsby Computational Neuroscience Unit - Alexandra House - 17 Queen Square - London - WC1N 3AR - Telephone: +44 (0)20 7679 1176

© UCL 1999–20112011